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        "author": "bab2min",
        "author_email": "bab2min@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: Microsoft :: Windows :: Windows 10",
            "Operating System :: Microsoft :: Windows :: Windows 7",
            "Operating System :: Microsoft :: Windows :: Windows 8",
            "Operating System :: Microsoft :: Windows :: Windows 8.1",
            "Operating System :: Microsoft :: Windows :: Windows Vista",
            "Operating System :: POSIX",
            "Programming Language :: C++",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries",
            "Topic :: Text Processing :: Linguistic"
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        "description": "What is tomotopy?\r\n------------------\r\n`tomotopy` is a Python extension of `tomoto` (Topic Modeling Tool) which is a Gibbs-sampling based topic model library written in C++.\r\nIt utilizes a vectorization of modern CPUs for maximizing speed. \r\nThe current version of `tomoto` supports several major topic models including \r\n\r\n* Latent Dirichlet Allocation (`tomotopy.LDAModel`),\r\n* Labeled LDA (`tomotopy.LLDAModel`),\r\n* Supervised LDA (`tomotopy.SLDAModel`),\r\n* Dirichlet Multinomial Regression (`tomotopy.DMRModel`),\r\n* Hierarchical Dirichlet Process (`tomotopy.HDPModel`),\r\n* Multi Grain LDA (`tomotopy.MGLDAModel`), \r\n* Pachinko Allocation (`tomotopy.PAModel`),\r\n* Hierarchical PA (`tomotopy.HPAModel`),\r\n* Correlated Topic Model (`tomotopy.CTModel`).\r\n\r\nThe most recent version of tomotopy is 0.3.0.\r\n\r\n.. image:: https://badge.fury.io/py/tomotopy.svg\r\n\r\nGetting Started\r\n---------------\r\nYou can install tomotopy easily using pip. (https://pypi.org/project/tomotopy/)\r\n::\r\n\r\n    $ pip install tomotopy\r\n\r\nFor Linux, it is neccesary to have gcc 5 or more for compiling C++14 codes.\r\nAfter installing, you can start tomotopy by just importing.\r\n::\r\n\r\n    import tomotopy as tp\r\n    print(tp.isa) # prints 'avx2', 'avx', 'sse2' or 'none'\r\n\r\nCurrently, tomotopy can exploits AVX2, AVX or SSE2 SIMD instruction set for maximizing performance.\r\nWhen the package is imported, it will check available instruction sets and select the best option.\r\nIf `tp.isa` tells `none`, iterations of training may take a long time. \r\nBut, since most of modern Intel or AMD CPUs provide SIMD instruction set, the SIMD acceleration could show a big improvement.\r\n\r\nHere is a sample code for simple LDA training of texts from 'sample.txt' file.\r\n::\r\n\r\n    import tomotopy as tp\r\n    mdl = tp.LDAModel(k=20)\r\n    for line in open('sample.txt'):\r\n        mdl.add_doc(line.strip().split())\r\n    \r\n    for i in range(0, 100, 10):\r\n        mdl.train(10)\r\n        print('Iteration: {}\\tLog-likelihood: {}'.format(i, mdl.ll_per_word))\r\n    \r\n    for k in range(mdl.k):\r\n        print('Top 10 words of topic #{}'.format(k))\r\n        print(mdl.get_topic_words(k, top_n=10))\r\n\r\nPerformance of tomotopy\r\n-----------------------\r\n`tomotopy` uses Collapsed Gibbs-Sampling(CGS) to infer the distribution of topics and the distribution of words.\r\nGenerally CGS converges more slowly than Variational Bayes(VB) that [gensim's LdaModel] uses, but its iteration can be computed much faster.\r\nIn addition, `tomotopy` can take advantage of multicore CPUs with a SIMD instruction set, which can result in faster iterations.\r\n\r\n[gensim's LdaModel]: https://radimrehurek.com/gensim/models/ldamodel.html \r\n\r\nFollowing chart shows the comparison of LDA model's running time between `tomotopy` and `gensim`. \r\nThe input data consists of 1000 random documents from English Wikipedia with 1,506,966 words (about 10.1 MB).\r\n`tomotopy` trains 200 iterations and `gensim` trains 10 iterations.\r\n\r\n.. image:: https://bab2min.github.io/tomotopy/images/tmt_i5.png\r\n\r\n\u2191 Performance in Intel i5-6600, x86-64 (4 cores)\r\n\r\n.. image:: https://bab2min.github.io/tomotopy/images/tmt_xeon.png\r\n\r\n\u2191 Performance in Intel Xeon E5-2620 v4, x86-64 (8 cores, 16 threads)\r\n\r\n.. image:: https://bab2min.github.io/tomotopy/images/tmt_r7_3700x.png\r\n\r\n\u2191 Performance in AMD Ryzen7 3700X, x86-64 (8 cores, 16 threads)\r\n\r\nAlthough `tomotopy` iterated 20 times more, the overall running time was 5~10 times faster than `gensim`. And it yields a stable result.\r\n\r\nIt is difficult to compare CGS and VB directly because they are totaly different techniques.\r\nBut from a practical point of view, we can compare the speed and the result between them.\r\nThe following chart shows the log-likelihood per word of two models' result. \r\n\r\n.. image:: https://bab2min.github.io/tomotopy/images/LLComp.png\r\n\r\n\r\n\r\n\r\nThe SIMD instruction set has a great effect on performance. Following is a comparison between SIMD instruction sets.\r\n\r\n.. image:: https://bab2min.github.io/tomotopy/images/SIMDComp.png\r\n\r\nFortunately, most of recent x86-64 CPUs provide AVX2 instruction set, so we can enjoy the performance of AVX2.\r\n\r\nModel Save and Load\r\n-------------------\r\n`tomotopy` provides `save` and `load` method for each topic model class, \r\nso you can save the model into the file whenever you want, and re-load it from the file.\r\n::\r\n\r\n    import tomotopy as tp\r\n    \r\n    mdl = tp.HDPModel()\r\n    for line in open('sample.txt'):\r\n        mdl.add_doc(line.strip().split())\r\n    \r\n    for i in range(0, 100, 10):\r\n        mdl.train(10)\r\n        print('Iteration: {}\\tLog-likelihood: {}'.format(i, mdl.ll_per_word))\r\n    \r\n    # save into file\r\n    mdl.save('sample_hdp_model.bin')\r\n    \r\n    # load from file\r\n    mdl = tp.HDPModel.load('sample_hdp_model.bin')\r\n    for k in range(mdl.k):\r\n        if not mdl.is_live_topic(k): continue\r\n        print('Top 10 words of topic #{}'.format(k))\r\n        print(mdl.get_topic_words(k, top_n=10))\r\n    \r\n    # the saved model is HDP model, \r\n    # so when you load it by LDA model, it will raise an exception\r\n    mdl = tp.LDAModel.load('sample_hdp_model.bin')\r\n\r\nWhen you load the model from a file, a model type in the file should match the class of methods.\r\n\r\nSee more at `tomotopy.LDAModel.save` and `tomotopy.LDAModel.load` methods.\r\n\r\nDocuments in the Model and out of the Model\r\n-------------------------------------------\r\nWe can use Topic Model for two major purposes. \r\nThe basic one is to discover topics from a set of documents as a result of trained model,\r\nand the more advanced one is to infer topic distributions for unseen documents by using trained model.\r\n\r\nWe named the document in the former purpose (used for model training) as **document in the model**,\r\nand the document in the later purpose (unseen document during training) as **document out of the model**.\r\n\r\nIn `tomotopy`, these two different kinds of document are generated differently.\r\nA **document in the model** can be created by `tomotopy.LDAModel.add_doc` method.\r\n`add_doc` can be called before `tomotopy.LDAModel.train` starts. \r\nIn other words, after `train` called, `add_doc` cannot add a document into the model because the set of document used for training has become fixed.\r\n\r\nTo acquire the instance of the created document, you should use `tomotopy.LDAModel.docs` like:\r\n\r\n::\r\n\r\n    mdl = tp.LDAModel(k=20)\r\n    idx = mdl.add_doc(words)\r\n    if idx < 0: raise RuntimeError(\"Failed to add doc\")\r\n    doc_inst = mdl.docs[idx]\r\n    # doc_inst is an instance of the added document\r\n\r\nA **document out of the model** is generated by `tomotopy.LDAModel.make_doc` method. `make_doc` can be called only after `train` starts.\r\nIf you use `make_doc` before the set of document used for training has become fixed, you may get wrong results.\r\nSince `make_doc` returns the instance directly, you can use its return value for other manipulations.\r\n\r\n::\r\n\r\n    mdl = tp.LDAModel(k=20)\r\n    # add_doc ...\r\n    mdl.train(100)\r\n    doc_inst = mdl.make_doc(unseen_words) # doc_inst is an instance of the unseen document\r\n\r\nInference for Unseen Documents\r\n------------------------------\r\nIf a new document is created by `tomotopy.LDAModel.make_doc`, its topic distribution can be inferred by the model.\r\nInference for unseen document should be performed using `tomotopy.LDAModel.infer` method.\r\n\r\n::\r\n\r\n    mdl = tp.LDAModel(k=20)\r\n    # add_doc ...\r\n    mdl.train(100)\r\n    doc_inst = mdl.make_doc(unseen_words)\r\n    topic_dist, ll = mdl.infer(doc_inst)\r\n    print(\"Topic Distribution for Unseen Docs: \", topic_dist)\r\n    print(\"Log-likelihood of inference: \", ll)\r\n\r\nThe `infer` method can infer only one instance of `tomotopy.Document` or a `list` of instances of `tomotopy.Document`. \r\nSee more at `tomotopy.LDAModel.infer`.\r\n\r\nExamples\r\n--------\r\nYou can find an example python code of tomotopy at https://github.com/bab2min/tomotopy/blob/master/example.py .\r\n\r\nYou can also get the data file used in the example code at https://drive.google.com/file/d/18OpNijd4iwPyYZ2O7pQoPyeTAKEXa71J/view .\r\n\r\nLicense\r\n---------\r\n`tomotopy` is licensed under the terms of MIT License, \r\nmeaning you can use it for any reasonable purpose and remain in complete ownership of all the documentation you produce.\r\n\r\nHistory\r\n-------\r\n* 0.3.0 (2019-10-06)\r\n    * A new model, `tomotopy.LLDAModel` was added into the package.\r\n    * A crashing issue of `HDPModel` was fixed.\r\n    * Since hyperparameter estimation for `HDPModel` was implemented, the result of `HDPModel` may differ from previous versions.\r\n        If you want to turn off hyperparameter estimation of HDPModel, set `optim_interval` to zero.\r\n\r\n* 0.2.0 (2019-08-18)\r\n    * New models including `tomotopy.CTModel` and `tomotopy.SLDAModel` were added into the package.\r\n    * A new parameter option `rm_top` was added for all topic models.\r\n    * The problems in `save` and `load` method for `PAModel` and `HPAModel` were fixed.\r\n    * An occassional crash in loading `HDPModel` was fixed.\r\n    * The problem that `ll_per_word` was calculated incorrectly when `min_cf` > 0 was fixed.\r\n\r\n* 0.1.6 (2019-08-09)\r\n    * Compiling errors at clang with macOS environment were fixed.\r\n\r\n* 0.1.4 (2019-08-05)\r\n    * The issue when `add_doc` receives an empty list as input was fixed.\r\n    * The issue that `tomotopy.PAModel.get_topic_words` doesn't extract the word distribution of subtopic was fixed.\r\n\r\n* 0.1.3 (2019-05-19)\r\n    * The parameter `min_cf` and its stopword-removing function were added for all topic models.\r\n\r\n* 0.1.0 (2019-05-12)\r\n    * First version of **tomotopy**",
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